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Title |
Pushing Novelty Criterion into Incremental Mining Algorithm |
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Author |
Ahmed Sultan Al-Hegami |
| Citation |
Vol. 7 No. 12 pp. 45-53 |
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Abstract |
Classification is an important problem in data mining. Decision tree induction is one of the most common techniques that are applied to solve the classification problem. Many decision tree induction algorithms have been proposed based on different attribute selection and pruning strategies. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for decision tree induction. In this paper, we propose an incremental tree induction algorithm that integrates novelty criterion during tree induction. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental classifier that reflects the changing data and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. We tested the proposed classifier and experiment with some public datasets and found the results quite promising. |
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Keywords |
Knowledge discovery in databases, machine learning, data mining, incremental classifier, decision tree, pruning technique, domain knowledge, classification, novelty measure |
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